
Class-agnostic attention learning with CVAE: DGAM [1]} aims to separate actions from context frames by imposing different attentions on different features using a generative model, conditional VAE (CVAE) [2]}. Formally, the objective of DGAM is:
\(\max _{\lambda \in [0,1]} \underbrace{\log p(y|X,\lambda )}_{\text{term 1}} + \underbrace{\log p(X|\lambda )}_{\text{term 2}},\) 
